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Record W3042990787 · doi:10.1108/jbs-02-2020-0044

Design thinking and radical innovation: enter the smartwatch

2020· article· en· W3042990787 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Business Strategy · 2020
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMindsetOriginalityMeaning (existential)Design thinkingProduct (mathematics)New product developmentProduct innovationValue (mathematics)Field (mathematics)EmpathyKnowledge managementProduct designBusinessMarketingComputer scienceCreativityPsychologyHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose This paper aims to expand the understanding of the design thinking (DT) field and provides evidence that DT as an innovation mindset centered on user/human needs is able to lead enterprises to the development of radical product innovation. Design/methodology/approach The study is based on an illustrative case analysis of four eras of radical innovations in the watch industry, from the mechanical wristwatches to smartwatches. Findings The findings from the watch industry substantiate the developed DT triangle framework for designers, managers and executives, enabling the development of radical product innovation. Originality/value The study provides evidence for the claim that human-centered approach (rather than design-driven, meaning-changing approach) in DT can successfully lead to radical product innovations. For this, this paper distinguishes between “need” and “meaning” in the DT field and reemphasize the role of creating empathy with users to be able to identify their newly shaped needs. Fulfilling these newly shaped needs would ultimately result in the development of radically new products.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.260
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it